In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined...

In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined...

National accounts data are always revised. Not only recent data, but also figures dating many years back can be revised substantially. This means that there is a danger that an important part of the central bank's information set is flawed for a long period of time. In this paper we present a...

National accounts data are always revised. Not only recent data, but also figures dating many years back can be revised substantially. This means that there is a danger that an important part of the central bank's information set is flawed for a long period of time. In this paper we present a...

Monetary policy conducted in real time has to take into account the preliminary nature of recent national accounts data. Not only recent data, but also figures dating many years back are potentially subject to revisions. This means that there is a danger that an important part of the central...

This study follows a novel approach proposed by Angelico et al. (2022) using Twitter to measure inflation perception in Colombia in real time. By applying machine learning techniques, we implement two real-time indicators and show that both exhibit a dynamic similar to inflation and inflation...

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to...

In this paper, the researchers have developed a short term inflation forecasting (STIF) model using Box-Jenkins time series approach (ARIMA) for analysing inflation and associated risks in Sierra Leone. The model is aided with fan charts for all thirteen components, including the Headline CPI as...